There is a debate in the investment community about the merits of Schwab including a cash allocation in its new roboadvisor offering. Let us leave aside the merits of roboadvisors (short answer: they are great for some people, while terrible for others) and focus on the idea of an investor holding a steady cash allocation as a percentage of total investable assets. Betterfront and WealthFront, two of the early movers in the roboadvisor space, have piled on Schwab. The upstarts argue the cash allocation was merely a cynical ploy orchestrated by Schwab to generate higher revenues from client accounts. Schwab meanwhile argues this is merely a prudent allocation. So, is cash a good, or bad investment in a portfolio account? The answer to this debate holds implications not just for roboinvestors, but for all investors alike and sure enough, I think there is a conclusive answer (as the title suggests).

“Cash has a significant chance of a negative real return over time due to inflation risk.”

“Cash assets can present a conflict of interest when the investment manager is advising cash and then re-investing it for its own revenue.”

“You never hold cash at Betterment, as we use fractional shares. That ensures every dollar—down to the penny—is fully invested in a diversified portfolio of stocks and bonds.”

The crux of these points are ancillary to the true debate. In fact, Betterment’s argument boils down to a marketing stance, more so than an investment argument. Cullen Roche at Pragmatic Capitalism nicely demonstrates how over the very long run, cash does in fact generate a nice, non-correlated return for portfolios; yet, this is merely the tip of the iceberg in defense of cash. I will do my best to round out the case here.

First, it’s important to note that Warren Buffett would strongly disagree with the roboadvisor assessment of cash. Alice Schroder offers the following take on Buffett’s perspective: “he thinks of cash differently than the conventional investors. This is one of the most important things I learned from him: the optionality of cash. He thinks of cash as a call option with no expiration date, an option on every asset class, with no strike price.” [emphasis added]. Here we have one of the foremost authorities on Warren Buffett labeling a cash allocation as amongst “the most important” elements of Buffett’s investment prowess. If cash is so important to Warren Buffett, who are these roboadvisors to say otherwise?

While this point is merely an appeal to (quite the) authority, it might be worth exploring how and why this is not merely fallacious thinking. For that, we can turn to Claude Shannon, also known as “the father of information theory.” I first cited Shannon in my post explaining how the Kelly Criterion can be used to size positions. Unsurprisingly, this was not Shannon’s only investment insight. One of the more interesting conclusions Shannon came to about investing demonstrates how it is possible to “make money off of a random walk” with cash being the secret weapon.

First let us look at the chart Betterment offered to support its case against cash, as it helps set the stage for why cash is so effective and what Betterment and WealthFront may be missing in building their story:

Notice something about both lines? There is nothing jagged or wavelike to them. Have you ever observed a stock moving in such fashion? Has any actual historical performance visually appeared as smooth these two lines other than Madoff’s fund? Sure, this is standard operating procedure for presenting simulations of what forward performance could look like in an optimized portfolio, but this is only effective as a rough guide. Reality assuredly will be different, and while no one can guarantee the end return will be different (despite this likely being the case), we all can guarantee that the path in getting from the bottom left to the top right will be different. The fact is, the path of stock price movements have consequences for portfolio returns (human behavioral consequences aside--this alone could be its own extended blog post). There is considerable evidence behind the notion that in the short run, stock market movements are merely a random walk. This is another way of saying that stock price movements will be noisy and volatile, with up and down days scattered across time following no real, predictable patterns. In many respects, this is one of the more important philosophical underpinnings behind the existence of roboadvisors in the first place. It should then be no wonder that this fact has serious consequences for the benefits of cash as a strategic allocation.

Shannon described a way to make money off a random walk. He asked the audience to consider a stock whose price jitters up and down randomly, with no overall upward or downward trend. Put half your capital into the stock and half into a “cash” account. Each day, the price of the stock changes. At noon each day, you “rebalance” the portfolio. That means you figure out what the whole portfolio (stock plus cash account) is presently worth, then shift assets from stock to cash account or vice versa in order to recover the original 50-50 proportion of stock and cash.

To make this clear: Imagine you start with $1,000, $500 in stock and $500 in cash. Suppose the stock halves in price the first day. (It’s a really volatile stock.) This gives you a $750 portfolio with $250 in stock and $500 in cash. That is now lopsided in favor of cash. You rebalance by withdrawing $125 from the cash account to buy stock. This leaves you with a newly balanced mixed of $374 in stock and $375 cash.

Now repeat. The next day, let’s say the stock doubles in price. The $375 in stock jumps to $750. With the #375 in the cash account, you have $1,125. This time, you sell some stock, ending up with $562.50 each in stock and cash.

Look at what Shannon’s scheme has achieved so far. After a dramatic plunge, the stock’s price is back to where it began. A buy-and-hold investor would have no profit at all. Shannon’s investor has made $125.

This scheme defies most investor’s instincts. Most people are happy to leave their money in a stock that goes up. Should the stock keep going up, they might put more of their free cash into the stock. In Shannon’s system, when a stock goes up, you sell some of it. You also keep pumping money into a stock that goes down.

Poundstone then offers a chart of Shannon’s performance in a 50/50 cash/stock portfolio rebalanced once per each unit of time:

It turns out the rebalanced portfolio beats the fully invested portfolio while also minimizing volatility. The example above is clearly a far more extreme version of the cash allocation and stock volatiltiy Schwab (or most investors) would take on in a real portfolio; however, even in more subtle form the effect is noticeable and real. Note how jagged, rather than smooth, these lines are. Jagged lumpiness is a reality we all must contend with in financial markets.

Long-term investors of all kinds need to acknowledge how hard it is to predict short-run movements in stocks. Even in a good value investing opportunity with an impending catalyst, one can never know with certainty which way a stock will move. We can rely on “asset classes” in the most general sense to earn a positive return over long enough timeframes, but we never can now in advance how long that long-run needs to be. Further, we must also acknowledge the unfortunate reality that it is possible for decades of stagnation on price appreciation even with a growing “intrinsic value”—we call this multiple compression. In such an environment (more so than in a trending environment), cash serves as imposed discipline: one systematically buys low and sells high when this kind of rebalancing is automatic.

Clearly rebalancing is part of the roboadvisors' strategy in switching between stocks and bonds when an allocation leaves a tolerance band; however, there are long stretches of time when stocks and bonds are correlated and meaningful periods of time where cash would offer not just a strong buffer against volatility, but an actual enhancer of return. Poundstone references how counterintuitive Shannon’s methodology appears. As counterintuitive as it may be, it is assuredly true and the benefits are both actual and behavioral. If you like better returns, with less volatility, then cash must be an important component of your portfolio.

Recently I had the privilege of attending Santa Fe Institute's latest joint conference with Morgan Stanley. This time, the topic was "Optimality vs Fragility: Are Optimality and Efficiency the Enemies of Robustness and Resilience?" The topic was both intriguing and timely, and the speakers were interesting, informative and a little bit more controversial than in years past. This made for an outstanding day. The audience in the room included some big names in finance and science alike, setting the stage for fascinating Q&As and stimulating conversations during the breaks.

This year, rather than writing one big post covering all of the lectures, I will break each down into its own entry. Here are the subsequent posts in order (and their respective links). Let this serve as your guide in navigating through the day:

I like to think about are how the lectures relate to what I do in markets and where there is overlap and dissention between the speakers. Further, I like to analyze how some of these lectures fit (or don't) with my preexisting views. I would love to hear what others think. Here are a few of my observations to get you all started:

Cris Moore's point that "best" is not necessarily optimal, and a confluence of models (what he calls data clusters) can yield better outcomes is extremely important in financial markets.

Nassim Taleb's suggestion that stress tests should focus on accelerating pain, rather than spot analysis is a powerful one that all risk managers should think about.

John Doyle's observation about the tradeoffs between robustness and efficiency is directly applicable to portfolio construction.

Rob Park's explanation of how algorithms are designed to express human intent, and the areas in which that can go has me rethinking my understanding of the risks from HFT.

Juan Enriquez opened everyone's eyes to how big the advances are in life science and the consequences this holds for the "secular stagnation" debate.

Dan Geer's explanation for why we have a choice between two of "security, convenience and freedom" online is both an enlightening and frightening call to action.

Again I will caution that these are my notes from the sessions. There is no guarantee of accuracy or completeness. I specifically focused on points that were intriguing to me, and purposely left out areas where the subject matter and terminology were too far removed from my competency.

There are 3 professions that “beat practitioners into a state of humility—farming, weather, cyber security.”

Cybersecurity—there is a dual use inherent to all internet tools.

Offensive protection is where expensive innovation is happening today.

There is an outcome differential between good

“The most appealing ideas are not important, the most important ideas are not appealing.”

10% of all internet traffic is unidentifiable by protocol, and more identification is simply not accurate.

Between security, convenience and freedom we can choose two, maybe, but not all three.

Some suggestions to help:

1 Mandatory reporting—CDC has it with regard to disease appearances and they store data with skillful analysis. It would make sense to have mandatory reporting for cybersecurity problems. With real problems, hacks, require them to be reported. With attempted hacks/near misses we can build a reporting system like the FAA has for near misses. Let people report this anonymously and get voluntary entrants into the program.

2 Network neutrality—is Internet access an information or a communication service? So far we have not named it a communication service, but in reality, which is it? This has consequences for whether there will be common carrier protection or a duty to monitor. Right now, ISPs have it both ways. They should get one or the other, not both.

3 Source code liability—“Security will be exactly as bad as it can be and still function.” There should be software liability regulation. “Intent or willfulness.” Build only liability for intent, not unintentional.

4 Strike back—research the attacker, build cyber smartbombs to learn about them. The issue here is the shared infrastructure.

5 Fall back on resilience. The code base on low-end routers today is 4-5 years old. Many networked components use old technology. Embedded systems should not be immortal.

6 Vulnerability finding has been a good job for 8/9 years. We as a society should buy out (overpay) for finding vulnerabilities. This can expand the talent pool of vulnerability finding. Are “vulns” scarce or dense? “Exploitable areas are scarce enough.”

7 Right to be forgotten. “We are all intelligence agents now…all our digital exhaust is identifiable.” Misrepresentation of identity online is getting harder and harder. The CIA wouldn’t have to fabricate an identity anymore, they can borrow one close to what they need. The new EU rule on this is appropriate, but doesn’t go far enough. “In public” means something very different today, than in the recent past.

8 Internet voting. Most experts think it’s a bad idea.

9 Abandonment. If a company abandons a code base (like Microsoft or Apple pulling support of an old OS), then it should become open source.

10 Convergence. Are the physical and digital one world or 2? They are converging rapidly today. Need to ask “on whose terms will convergence occur?” The cause of risk today is dependence. We will be secure if there can be no unmitigable surprises.

Security breaches/viruses follow power law distribution. Target and Home Depot both fit on the curve.

Historically on the planet there have been several hominins existing at a time. Right now humans are the only species of hominins.

Typically when there is only one species, that is a sign of impending extinction.

The difference between humans and Neanderthals is less than 0.004% on the genomic level.

Differences are in sperm, testes, smell and skin

There was an experiment in Russia to try and breed domesticated wild foxes. They took only the friendliest foxes and bred them amongst each other. Within a few generations they got tame and were worthy of being pets (more on that here).

We can now sequence and acquire genetic data 3x quicker than our capacity to store it. We’ve sequenced about 10,000 human genes today. We will start to find more differences soon.

Synthetic Genomics has developed a cell built that can operate like a computer system. It’s a cell that executes life code.

It may be possible to reprogram a species to become another species.

It’s like a software that makes its own hardware.

Algae is the best scalable production systems for energy development in a constrained world.

“We are evolving ourselves.” In science, “there are decades when nothing happens and weeks when everything happens.” (a questioner in the audience pointed out this quote comes from Lenin).

Q: “Do we have secular stagnation?”

Enriquez: A resounding no. Today there are people who are smart, creative, with scale and ambition. Lots of great things are happening in the sciences. We are as advanced as ever, and increasingly so. 1 problem is that with technology, our interest in sex different than it used to be, and sex is not keeping the developed world population moving upwards fast enough.

Started program trading with a spread algo between Deere and Caterpillar, under the assumption that fundamental drivers were similar and spreads will revert to mean.

In executing this algo, felt orders were being copied by someone else.

Today, 70% of total US volume is algos.

How do algos introduce risks?

Problems occur when you can’t predict.

The algo ecosystem: the number of possibilities grow exponentially when algos interact with other algos.

1 runaway algo problem. Example-on Amazon there was a $1 million book. Someone raised the price in marketplaces of another ever so slightly and that triggered a cascade where this book ended up listed for $20 million (the story of how this happened is fascinating and told here)

2 Flash crash – unpredictable interaction of algos

What is an algorithm? It is a sequence of logic statements. All algos are created by humans. They do what people intend them to do. Intent=important. Humans are driven by incentives, algorithms are driven by human intent.

The technologist needs to understand the human goal, or else risk is introduced into the system.

IEX introduced a 350 microsecond delay on an order reaching the exchange.

The broker’s dilemma: brokers were matching orders between buyers and sellers, so brokers created dark pools. Broker A gets the buy, Broker B gets the sell, what’s the incentive for Broker A to trade with B?

In today’s market there are 11 exchanges, 40+ dark pools (IEX right now is a dark pool, but will try to become an exchange eventually).

Exchange dilemma: exchanges facilitate issuers with investors. Exchanges are supposed to be neutral to all participants, but now are for-profit companies who build services for specific customers. This is not the intended purpose of exchanges, and biases these exchanges towards one kind of participant (HFTs) over another.

2 gaming automatic trader-based algos. These algos took advantage of transparent inefficiencies in the first generations functionality.

3 counteract generation 2. A trader who wants to buy size needs to game level two algos in order to hide intent and execute efficiently.

Participants send orders, but they don’t arrive at the actual exchange at the same time.

At the micro level, markets are deterministic (opposite of physics).

Latency arb—in a distributed system, race conditions matter. HFT aims to exploit the race. Exchanges need to know where the market is before pricing a transaction. Introducing the 350 microsecond delay through a fishing-line like fiber. In doing so, assume the order is not fast. And then figure out where the market is.

Resistance to IEX so far has come from 2nd generation algo programmers.